An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting

Download

index.pdf

Date

2011

Author

Ceylan, Uğur

Metadata

Show full item record

Item Usage Stats

196
views

104
downloads

The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of collaborative filtering. The content-based part of the proposed approach exploits semantic similarities between items based on a priori defined ontology-based metadata in movie domain and derived feature-weights from content-based user models. Using the semantic similarities between items and collaborative-based user models, recommendations are generated. The results of the evaluation phase show that the proposed approach improves the quality of recommendations.

Subject Keywords

Recommender systems (Information filtering)

URI

http://etd.lib.metu.edu.tr/upload/12613754/index.pdf
https://hdl.handle.net/11511/20770

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A Multi-objective recommendation system Özsoy, Makbule Gülçin; Polat, Faruk; Alhajj, Reda; Department of Computer Engineering (2016) Recommendation systems suggest items to the user by estimating their preferences. Most of the recommendation systems are based on single criterion, such that they evaluate items based on overall rating. In order to give more accurate recommendations, a recommendation system can take advantage of considering multiple criteria. Beside combining multiple criteria from a single data source, multiple criteria from multiple data sources can be combined. Recommendation methods can also be used in various applicati...
Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010) Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user proﬁles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
A recommendation framework using ontological user Yaman, Çağla; Çiçekli, Fehime Nihan; Department of Computer Engineering (2011) In this thesis, a content recommendation system has been developed. The system makes recommendations based on the preferences of the users on some aspects of the content and also preferences of similar users. The preferences of a user are extracted from the choices of that user made in the past. Similarities between users are defined by the similarities of their preferences. Such a system requires both qualified content and user information. The proposed system uses semantic user and content profiles to mor...
A Hypergraph based framework for representing aggregated user profiles, employing it for a recommender system and personalized search through a hypernetwork method Tarakçı, Hilal; Manguoğlu, Murat; Çiçekli, Fehime Nihan; Department of Computer Engineering (2017) In this thesis, we present a hypergraph based user modeling framework to aggregate partial profiles of the individual and obtain a complete, semantically enriched, multi-domain user model. We also show that the constructed user model can be used to support different personalization services including recommendation. We evaluated the user model against datasets consisting of user's social accounts including Facebook, Twitter, LinkedIn and Stack Overflow. The evaluation results confirmed that the proposed use...
Extending singular value decomposition based recommendation systems with tags and ontology Turgut, Yakup; Toroslu, İsmail Hakkı; Department of Computer Engineering (2014) Due to increase of the volume of data related to user ratings on items, in recent years, recommendation systems became very popular, which uses this data in order to rec- ommend items to users in many different domains. Singular Value Decomposition is one of the most widely studied collaborative ﬁltering recommendation techniques. In some applications users are also allowed to enter (sometimes free) tags in addition to their ratings on items. Adding tags in addition to regular users’ ratings on items have a...

Citation Formats

U. Ceylan, “An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting,” M.S. - Master of Science, Middle East Technical University, 2011.